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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14

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Paper Title: LUNG CONDITION PREDICTION USING STROKE ML ANALYSIS
Authors Name: MAGENTHIRAN R S , Naveen Kumar S , Nagendran M G
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IJRTI_211606
Published Paper Id: IJRTI2604171
Published In: Volume 11 Issue 4, April-2026
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Abstract: Abstract — Stroke-Associated Pneumonia (SAP) is one of the most critical and life-threatening complications following acute stroke, significantly worsening patient prognosis and increasing mortality rates. This paper presents a machine learning-based Clinical Decision Support System (CDSS) designed to predict lung condition risk in stroke patients admitted to intensive care and general wards. The system employs a comprehensive preprocessing pipeline encompassing missing value imputation, feature normalization, and class balancing using SMOTE to address real-world clinical data challenges. Four supervised learning algorithms — Logistic Regression, Random Forest, XGBoost, and a Deep Neural Network — were systematically compared using stratified 5-fold cross-validation. Random Forest achieved the best performance with 92% accuracy and 0.97 AUC on the test set. SHAP (SHapley Additive exPlanations) analysis was applied to provide clinical interpretability, identifying key predictive features including dysphagia severity, GCS score, age, and mechanical ventilation status. A web-based dashboard enables real-time risk prediction and clinical decision support at the point of care, facilitating timely preventive interventions for high-risk patients. Keywords — Stroke-Associated Pneumonia, Clinical Decision Support System, Machine Learning, Random Forest, SHAP Interpretability, Risk Prediction, Electronic Health Records
Keywords: Stroke-Associated Pneumonia, Clinical Decision Support System, Machine Learning, Random Forest, SHAP Interpretability, Risk Prediction, Electronic Health Records
Cite Article: " LUNG CONDITION PREDICTION USING STROKE ML ANALYSIS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b249-b254, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604171.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2604171
Registration ID:211606
Published In: Volume 11 Issue 4, April-2026
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Page No: b249-b254
Country: Perambalur, Tamil Nadu, India
Research Area: Computer Science & Technology 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604171
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604171
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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